{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 您的F1分数数据\n",
    "f1_scores = {\n",
    "  '中药': 0.8543,\n",
    "  '方剂': 0.8121,\n",
    "  '临床表现': 0.7890,\n",
    "  '中医治疗': 0.7654,\n",
    "  '西医诊断': 0.7432,\n",
    "  '中医诊断': 0.7210,\n",
    "  '西医治疗': 0.6987,\n",
    "  '中医证候': 0.6543,\n",
    "  '其他治疗': 0.6210,\n",
    "  '中医治则': 0.5876\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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wYDul858nIjcuhH936NDB0lwoYMr3D4QaU7yUUGMi2EhlIVIKiF7DboBCp77rC5+pqILowUkoaWCcVFPXi0g1X+icgqY+TJ8UZOqyYV/MhRqT6MCJNNCNijmL006iCi666CIrmE1UIY8pdk7rbtIreOxTxOluCToVjR6cPhMpRVQHTU84mSb6g8gooqWIKiTSkyhp7IQ9AtAxqVWrVnZf4xMZ/PfoIwGIRiOqkMhcbIUiwETeEJXDT79vgLJly1qEO9Hu2gNEHr9XpoRF+JrCWk9KEen8zHNEFRJFTQ09otnpwMe6kytXLpcvXz6LAOE+yG4ig+zm7ID1w++fyQihThRp4ZQrYR9NHTYiqYhAxJZIi/U1jPPmzWsR76T1i/hFopT4R/xES/FynDaKABM+6etIAA4BzhlhrDjRpCWxGcVxwPH28D4R+W5uQD2VPn36hMaL1D1CVWvUqGELJqljLJ4UzCSVLFyYIi3M1/ZQvnXkNqDUf2BTiaNGKou3o6NHj1r4PSl6LKg4c9S/waYoGIyw6x1qj8Ym+pAyQS0vnGqKmyNy0MzhySeftNfZ8PgwfZxx7MgLJ+LM2b17t81VCLiAsEGdFTruIdKSTkFNPNLAnn/+eZvbOOhgTaJmISIhxWhLlSqV4HMlGkYG7xCEO3I4zDhuHGwg1LL+U5yZscB5Ju2Igyz2AdT7evvtt22OC0fjc+aw1vjvEZvx9xkTxD9qsq1evdoORqhjyDiRDuvXfWoX8V5SX7TORGefxgEVcxXOsodDWlKNqR/JoSJjweEhDjTiSLp06aysAj8TI7s5c2Q3Zxfsn8PLyLC/Rpxq2LCh7ZkpsUABetIrsSt8ImD/rfIX8Y8sSPwtOM90AKGwL53ciB4gaoOTaESN119/3d7HRIDzRqFTnAcKauN0s/nkvogsCE44A/7EpnPnzm7WrFnm1CH84TzwOhseTgY4zUEAweGjpgdO9tatW0MROB5tciIHGxcKkzZv3txEJ+pBIGDQHIAIHJxo6q8g3OJEE21APjzjw6KKQx6OxiZyIJoztwGn1NgNAhQbF7qEYTOciHLaxvhwuk0EDvjoNwrWY3enchbEf3PcKCaPGNipUyfr3EonPRwBNpa8BghT1DBEwMVZ4ASUG3WMENyp84EgIiILNoAgSC1JIm85hGJPQGMGIjxxCIj2YOPP2CEsYh+sOYwd+wVuRE0xx4nIQTQAazljwjyG/SByYAes9ezfWD+oIYmtEJnLHEY0DvBagQIFLMKA9UpEdl5jzUDwQJDCWR47dmzodewGe0DUIMKdCBA6vbIfQORgP81e4rLLLovp33EuIruJb/BhqHWLP4N4SL1I/EwEW/ZnQE1Wom4JguAQHrAZ1hzqTuEXibOIf5HiJ5IxixcvDsqXL2+57bly5QqeffbZBDVxUqdObTWMPJs2bQoef/zxYMmSJUGfPn2CUaNGBUePHo3R1Z+bbN++PShWrFhQq1Ytq6HSuXNny2nn/l9B/S/GwtcpIh+ezxGRhe+Vmg833nhjkD9//uCpp56yujb58uUL5syZY/UHqCFF/YjwmlMjRowIrr322uD333+3x9STENGBul7NmzcP1YNijG644YYgU6ZMQfXq1YOBAwfa89OmTbN6bNTMeeKJJ6yuxD/VDBNnxrJly4K0adPa+sG6Qo0IanxRVyUc6hTyOrVYGK8CBQrY+8JrSKjOV2RZs2ZNkCFDhqBOnTpmL9QgzJYtW1CmTJlg+PDhofft2bMneOihh2zO27p1q92uuuqq4O23307weRqfyEFNtSxZsgRffvmlPV63bp2NEWtQ06ZNrQYoPP/888G2bdvsPrXYVEcyuvi6Q9T0ZM9Gnc/wfRf11dgveFv49ddfrZYR4/LKK6+E7I6arSLyyG7im9dff938T1+/GB8GPzRFihRW0zPc91y5cqX9ZC8wc+bMmF2zODMkSol/XFDHjRtnm1EKMLLhhGPHjtnPnj17mjOH4+ZhgX366aeDokWLBt99912Mrv7cZu3atSZcUDg+R44cCQQpxo0x84uoB0EEJyK82DbIOYisvVAs+4ILLjCHGSebor84ZGwuc+fOHbz33nsJhFqKllIAeMyYMSd9psYm8vCd4kgzd3komo3Iy6aHgszARgiHGrAlimqGb4JEdAoAI3pQ+B+7QAyk+D9iOs4bdsP8ReFzxnHq1KnmQCQWpETk4butWLGi2Q3jQGMT7OLrr78+6b379u0L2rRpY4VlcSK8g81naIyiQ5MmTWxtAcTcrl27Bp999pk9/vTTT0/5O4iFNBAQ0YP9AHsADjw6dOhgxf+xHYT2NGnShAoye5jbEKYQRxKPjWwn8shu4hcO/Sg27w+lWG9oqvHMM8+Eis4nhgNhCp+D7OXsQ+l74m9z4Dds2GDhraQeZc2a1UKKaZNOmgvhrtT6ILWC+iqe3377zdKTSKdQDanI4utF0RaYIrKkUZDTTk0PP24UnKU1KrU7qHnjIfSYMUxcjFFpYZGzl/3791tNG+qr8F0Tkt++fXtLCSMsnLB96rIRHu7HgborFHCmUUBiNDaRwde/8YXiGRPqfAHpRdSKeuyxx1zHjh3dJ598YrZDIwfSkbEharGRarly5cpQ2p+IHOG1OFhbqD/Ec6TjYVu0RKcRAzcKA2/atMnGETsitaJ27do2xrKX6NgN6RN8t6TfMcdxn4LMpFAwJtTAoVEDqcmkwzL3PfroozZWjCEpmX58NEbRgTQw0lmp20UxeVIoKZ1ATRzSw31ReSCNnBQX0vspni2iYzd8x6TkU/dm3rx5Nl+xNyZND1uilT3pyb5OIZAyjr2wX2CvHY5sJ/LIbuK7URDjAaS/4lMyNvg3+JnU8sKuvM1Ru5C9tjh7kSglTgLjZjJAkGLypYA2whSbUAo0Ustj0aJF5nCzsHbv3t1+zzvZ1Fih5kfJkiVj/Jecm8WzASGQwuV0A6GDi8+/Llq0qNUtYKKmw4ufsPldHG+6ioULiCIy+DpD1JBi44noQb0VxoOcdupIYEMIVogcbGi86PvUU0+ZACIBN3r4zbz/iQ0sX77c6t4heOAYsMHBkW7durVr2bKlOQzhhTUZV2rqJa71Jf47iH/MSRQu/+KLL+w5bIMaH8xnzFuXXHKJ2Rc3BFycNup7APfp6gpy2KJTuzDcyWbDT21ChEMcNMYGkYoDKooyU2wW5w4oDMwaxZwnwTCy8N0j/lHo19dao+j/nj17bD7jkIpDDrq4jRgxwho3sHejZg4wh9HBsn///tYVUUQWahQC3zGONHXXgL0ZY8BeDLugwxswt/nOrn5eYw/hG2yIyCC7OTtgPBCdgK56CxcudG+++abZCPsF9s3UyeWwBP+UvZrv8I7vSY1j0Jpz9iFRSpwEhsxpAI4Zhs8pKLCI9urVyyIJKDZLK04KnybuAge+05iIHP77pU0txXz5SaQUwhNOHCJgkSJFbCGloxsLKY40zh6/y43FFtRKOLLw/z9Ffe+8807XqFEjK4JNJ0o2pHz/CLdEdLARQtTduHGjLbIIvRQJpiCjiA6HDh2yiE42M2z06WSEnRApReQTDgDONCejCIQ8R+FS3kvBeW93iPA4FdronDnMPwhORNcgxnKfzb4vPktRU25eqEXIxWFAJAnvkCSiB9FqOGycTOPEASIU4hNjQCFzxHQeEz3I4QgRUgULFgx9hhdwNV6RBaeMyM4XXnjBuoXCli1brCU66wsNAmhQg2NNtA2PK1WqZIWCWZ88jB0CiYgcRGuwP+ZAis6g2BBgIy+//LKtO3SkxF5oduJFXD8O7BUodM66BNqrRQ72XbKb+IWsDwQluuk1bdrU/t/Hl+QAkb0zezD2c3R3xS9du3at+akdOnQwYYoDRKLbEneuFmcP/7+3ohBhYNy008RxZrImYgroNMHETicQJmZSkVh8fRc4EV3o4obYQVQOEWlsYBCoiO4gVYJNj3fiOEHgxIFoNk6BvNMHchIiDyHF2AfiLZvOTJky2eKKYIgt0bWK8WHscNYQCLGrihUruptvvjnWl3/OgjOA0IGoUbhwYZu36ArKppNTUKI+GS+iOhDcOb2m6w6vETrO+3DMsRnNc5EBUSNDhgyWFh4OUTicQLPhRKzFnhA/SNejQxWiFKehrE+KvokuWbJksRb1/P/vU4tJb0GYYkw4pcYxQzxkTBhTTrLlEEQPvmPEDGyEfQBpxR4iPYmcZi9A2/pBgwbZoRU2hENHBAHOG0KiiB7YBx30mMtI2/ORthxuIECR2j9nzhw74OX+c889Z2NC9CddxLAr1iiP5rjIQdkLBCjS8j2ym/iBPTPBDnQ89qnezHf4MwhROXLksMP3Nm3a2J4NZs+ebdG6HMITBYftyGbOYmJd1ErEL3QMGTRokHUNef/994MjR45Y0bnRo0db0VkK0nbp0iXUoUJEHwpnUwA4vAtVx44dg0svvTTUoYJCmR7G79Zbb03wnDhzfNc1uuVhF+EcOnTIipdSDJPCmXXr1rUi87B58+bgf//7n72HWzgqyhhZ+K79d5p4jCiSSdFyOiHRUTRlypTWPADmzZtnj+nKR3ckEVko4ksHI5plhHcvrF+/vjVtmDBhgnWh4nXWIOa8woULW9F5usAOGDAgptd/rkMH3fDC856JEydagVnsav369VbwnM6iFGQePHhwsHHjRmt6krjBhois3STuZjx06NDg0UcftX3Z/v37zXYuvvhi2xcATTbOO++8YPLkyQl+T+tN5O3mVN8pnUS/+uoru//DDz/YGJYtW9bez/OtW7e2bpUlSpSwZhsejU9kSfx9ym7iA/yWxN11/R6NRlnswWgSxLxH90r8Tg8d+OjOd/jwYWuIIs5+JEqJvwXHmQ5IdDjKmjWrTeQeJm06HTz44IMnbZREdMFp8I4DP6+++uqgcePGCd6zevVq6yzCIuu7JYozxzvSbGRYEH23Nj8e2AW2QrthoBPfbbfdZqIHTt1NN91km9NTOX4iMkyZMiWoUaOGOc3h0FqbLqJ0QqJDGNBth06hP//8s3V9Q5DCAQ9HYxUZaK2dPXv2k7oa9enTJ6hZs2YwadIk6/DG+NHpla5IbDj5PYR1BBCEXpG0dnPXXXeZXbCecCAFrPl0FmNuw6kYP3580LJlyxhdefKzG5zjOXPm2HqCmEH7dA5JYMWKFUHp0qWDVq1amWONXYno2w32EM7w4cNtPkNM9/sE1v4hQ4bYuO3evdvE9owZM9p7PFpvIkf4wYc/yJXdxAccOrH34qA2sZB72WWX2SEVIq4HX4aO4x9++KF1dmX/Lc4tJEqJf6Vk9+vXzxbexBBVEB65I5IWFlggku3yyy+3qAIPpwucYGt8IgcOMiBgsJg2b948weuIf4i4/uRnw4YN9r569eqZ83bLLbco0iMJHDja0CdutU3r+h49egTTpk0L5s+fb7bjo6MQ1kuVKmUb0XHjxsXoys9tZsyYEeTNmzc0LmxIPX4cYOzYsbbZRBjEXny0Wq9evcyREElrN6+88kpw9913myiI6NSoUSNzCogwpHX6999/b++rUKFCAgdCRN9uwu/DggULTGSHRYsW2dpzxx13JPEVJy/+ym6WLl1qh7hE6TZt2tRsB2GK+YxxGjNmTFCuXDkTQYim9kiQigw+Ot3DAYffLycW3WU3SQ9zF4dMnTt3Puk11pXly5fbno0xIzLXH0Z16NAhyJkzpx3EexS9du4gUUr8K/xJgl80NQnEFzgGbIwSn+woQipyrFu3LnjggQfsZI2NJKmricONOZULTxV77rnnLMrAR4OkSJHCnhPRc+AI8fYOwq5du+x2Kj7//POgQYMGwY4dO+xx3759g4suushOskV0x4WUb4QO0ic94WsKwuB1111n9vLWW2/ZPEYaBeI7yHFLWrvxY4MYT7TUvffea2nhOAhACszNN9+siOkY2E1i55qodn94wqEhqWEitnZDahGR7OwFKlWqZJFSiLyUXSAqx6N5LXL+CsJF1apV7fHevXuDNGnSWGaH3xOHrzeym6QXpK6//vqQEAgzZ8606OhTMWvWrAQiIesOEe+MoXzRcwuJUkKc5fiNDJsev5BqcxNZWPhYMFu0aGGOMqH64eIF0TeFChUKRQ147r///qBKlSqhx9TL8ad1IroOApGC1PUiJP9U4fxsTom8CU9JTlw/QkQmkiA80gMx8MorrwyefPLJ0HhQzwPCN5jU0CH1lQ2pdzS0AY2d3XjBCcetTZs2Ng8SAQIff/yxjStjqTFKersJ584777TfTYzGJTZ240UQ7Oa+++4zu6EuTsOGDU3Y9WjPFln43q+55hrbf40cOdKioTmEIp3Sj0l4ap/sJmkFqfBDXfbTZBL8XbZOgQIFrLyCJ/FeW5wbSJQS4hyBTU54XQIRGfzGBQeA0zQWU0LvO3XqZOl5RE5lzpw5wYIZHl1FdEFitNGJLAgYOHC+6QJpEoiE3oFD0AiPUPPfPynJCIeJkYMQGaZPn26OGykRQDHsfPny2akoINDy+mOPPRYal3BHgeg10sREfNiNtwuiPCpXrmzF6UV82E344ZRqe8Wn3fA8wgi2E16YWetNZNi5c+dJeysOnq644gqr9UmNXPbJ1C9MDFFsspvowvdPtFq3bt1CzxF5iyDoIaOA2qsevx/gAOTpp59O4isWSY1EKSHOcvwiTN0v1fSILH5BZANJnQHfmYWuOWxiqAWRNm3aYOHChQnej1BFOp9HIlR0HTgchJdeeinkwPHYF8/EgQ6v2xE+FqSGEeavU7foRnpQR6127dom6hIp4As2s0ENPzH1aeLejhIXqRWxtxs/RsxvKgQcX3YDFKKn666IL7vx0TlEUrF3oFEKSJCKDAiDFC6n2DxRbIgdfLdESJ1//vkhAR1hl9RwInY4VKQ+nrcbarCK6MD3zffOvEV9LyI/fcStt5GBAwdatCE2lRi6IZOOiSCv/fS5i0QpIc4RmMhV1DzyEHaPU/D4448neJ6CzCyo3OgIUqRIEesIwoaUhXX27Nmq6ZVEJ9YUykZgYixIcaH7ITVu2LyQIvHII4+EfiexjaibW+QhSiB37ty28QRqsTE2RBMSnUZaXpkyZexkGhuhpgcbVIoD+86h4WgTGn92E47GJz7shjTKcDQu8Wc3EqGiA2Ig0YPDhg2zQyaKyBOVRsfdzZs3B8WKFbMUMUQpIm4odo5d0eWNgtrhyG4iL0iVLVs2ePjhh0NRT37fTCofDU3IPiDd0kcQsgfg8DfcZnzHZHHucp4TQpwTFCxY0OXKlSvWl3HOsWDBArdlyxZXv3790HOzZ892vXv3dtmyZXOFCxd2l19+uWvVqpUbPXq0Gzt2rHvnnXdc7dq13fnnnx/Taz+XmTt3rmvatKl7+eWXXbFixVzKlCnt+XHjxrl9+/a5o0ePuhw5cthzLVu2dA899JC76aab3JQpUziMcb169XJ//PGHq1Onjr2H58SZ4b/D119/3R07dswVL17cHpcuXdoVKFDA1apVyzVp0sTlzp3brV692qVPn96VK1fODRgwwK1fv95dcskl7s4773RPPfVUgs9NkSJFTP6ec5FI2U04Gp/4sJuRI0cm+FyNS/zYTc+ePd2JEyfceefJ7Yo0f/75p0uTJo0bNmyYW7Nmjd1ftmyZ27Rpk1u7dq3LkCGD/Tx48KAbP368a9u2rcucObON48UXX6z5LIrw/3716tXdDz/84IYOHWrPXXXVVS5v3rzurbfecqtWrXK7du1yDz/8sGvcuLF78sknXalSpdwtt9ziDh8+7CZMmOCaNWtmv5c1a9YY/zUi2mh2FEKIU2xyPPXq1XP9+/d3JUuWdCtWrHA7duwwB+GNN95wPXr0sA0Pi+ehQ4dsU3rdddfZgnvBBRfE9G8417nyyitd6tSp3cqVK+0xDsBll11m4iwbIQTBvXv3mpOHgPjjjz/a5qhKlSo2fuvWrQs5FqCN6JnbDN8hjterr75qTvP111/v9uzZYxt/RFrG4o477nB58uQxZ7t8+fLm1H3wwQdu6dKl5mTnzJnTzZw5M9Z/zjnLmdoNIki43YgzQ3aTPOxmw4YNLlWqVLH+M865PRqCkhf6EDsWL17sHnnkEbdw4UL3888/m11xyHHkyBETQBijGTNm2Ps5SMyfP78dLIrowNyGGHXgwAE3ePBgew7RKUuWLHZ/yJAhJhb+/vvvto/GfjjcRVz89NNP3cSJE92DDz4Y479CJBmxDtUSQoh4IryODbU5jhw5YvdHjBhhtQlGjRoVal1LZzBqFhD+TYHZ7t27n9R9R0QWQrl9ODd1V7JkyWIFf7/77jsL3acgPWH67du3t9B9imqGF5tv1KhRguLmCtU/c/x4EKZPCL5PiaxevXpQvnz5YMeOHfaY1FbSjaBEiRJWe81DAVq6Wyb+TBEZZDfxh+wm/pHdxC98z6SEeTuB1q1bW2FzfpIStn379uD2228POnTokKBbJcXmwwuea1yiu5f2tkPdLtLySLGkBAYlL2gIVKpUqQTz2osvvmhzHa+J5IMipYQQ4v9BGL6PAujbt6+d8Pz222/2uGvXrhYezqk0p9j+ZI50Ck6vu3XrZidxW7dujenfcK6fjnIqym3btm2uTJkybt68ee6ZZ56xtArGoEaNGpbOMmbMGDuNYzwvuugi+31SMNOmTWsh4f7zFCF1ZviTak45STMiOsCnRDI2RAw2bNjQffvttxZ1SPqEh/QJaNCggUUcTp06NcE4i8ggu4k/ZDfxj+wm/uA7JIKmbNmyluJFZM0VV1wRej1fvnwWdXPhhRfaPo0INiKjSOUj9fizzz5zFSpUsKg10mX9Z2pcIgsRatjC9u3b3ZIlS8x23nzzTTdixAj73nmdKCmiO6+++mqzE8YOpk2b5p544gkbN14TyYhYq2JCCBEP0N6ZAoxAUXMKMIafeHp4jWKzREetXLkyuPrqq0MncCo0nzTQaTJbtmx2mgaMA6dwnLx99tlnofdVqFAhGD9+vN2vV69e0KpVq9Briig4c/x3SDMACsuGdy9asmRJ6D4n1UQT7N69O9Rl59prr7WuYdicxiVpkN3EB7KbswvZTXzgo5nYb2XMmDHIly9fgi7HsGnTpuCZZ54J5s+fb9FrFJ3ftWuXReZQWJu9W506dULv17hElvDv88svv7SotfPOOy/UrZJuh5kzZw4mTJhgxemB7ILs2bMHhw4dsu6jREj5IucieSFRSgghgiCYO3eubfhpw01HHRbKrVu3hkKQO3fubE4Eiy7h+ISNL1682JwDOoaEh38rFDyy0InFb/779u1rLdH5mTJlymDOnDkhR4HNju+SSCcrQsJp+0y3Km4ebUTPHG8XbCTprIOTgL0A7bgZC98mHWjnjNBLl9Bnn302GD16tLW7b968eeg9GpfIIruJP2Q38Y/sJn7t5ocffggqVaoU9O7d2+wFIZDx8t8xgu3Ro0ftPmNI2h7pljt37rTXEK08GpfIgsiEgMvhLGJhgQIFLCV56tSpQapUqUK2w94aURfbIXUZ8bBPnz5mQ6QxS5BKvkiUEkIka7xD4Gt3FC1a1OoN4Azccccdthml3TMtbG+66SZbeGnBzXv43W3btsX0+s91qN1RsWJFEwXbtm1rG382pjBkyBBzFPwpnK9bgLDIuEyaNMmiDXRiHb1ID2rfsKGkvTYn0dgMtTpw2nAeqMHmqVKlio0Hp9a8r2PHjid9pogMspv4Q3YT/8hu4hNfN4rIQQ4EPVmzZg1uvfXWYM2aNcGxY8fsOepI+bqfX3zxhYke1F0L3+vp4DDycEjLvNasWbMgV65cJqJ7sJ3UqVOHog297TzxxBPBgQMHzNbYf0uQSt5IlBJCJFvYsHBa7UPu2exT3Nzz008/WfQUJ22EjCNEcTJ3/PhxCwEP33yKyOM39MuWLQuKFCliKRQIguA3oJzCIRi+++67oc0O7yPaoG7dunLgIoz/Don0QKTFSdu/f789R2oEG03GABYuXGj2Rai+B6ea8aIQbeLPFJFBdhN/yG7iH9lN/IF4hBhbuHBhi2D3kWmA2MStVq1aJkwhOiFeUTx75syZofcxRgiGInr4/9fHjRsXZMiQwcRZLwx62+nZs6dFhvqDXFL5SNsLjwYVyRtVRBRCJDsQ5IFimNWqVXPLli1zb7zxhhUtpeW5fw+FSimMSbHZdOnSWZFTWtvedddd1vaZ10V0CC/au2LFCnf06FFXunRpt2jRImsXTAFToJgmhU59QUwKalJIs0uXLvb7o0aNOunzxH8Dm+A7pIVzzZo13Z133mlFlml7jh1ly5bNCpT279/fWtVTALhEiRJu9uzZ1sIeGD/GZNKkSfZY4xJZZDfxh+wm/pHdxKfdUICcZjOMB2OQO3due61NmzZWLJsC8zQF2Lhxo2vevLlr2rSp6969u2vcuHHocxij1q1bhz5TRKdpw4YNG9zTTz/tWrRo4bJmzWqFzL3tfPfdd+7dd991jzzyiDUIwj5uuOEG9+qrr9qem3mtYMGCsf5TRKyJtSomhBCxqlFA3juh3tTpaNy4cdCgQYOgR48e9jq57RTJ5GSbYuaEfxMiTlgy0VPhEVUKBY8ejz76aFCyZEmrE0FqCykuXbp0seK/jBtj48PyiWDzJ3aMq0cn1pFj7969QaFChezU00OKS/r06a1uBzAuvCdt2rTBe++9F0yfPt1OugnhD0fjEj1kN/GF7ObsQHYTXxBl06hRI6sNNWPGDItUJxKqa9eu9jrRaUOHDrWx8CmxW7ZsCWbNmhX06tXLUsNE9PB73/Xr1wcFCxYMJk6caI/ffvtti2BjPIj8LFasWDBs2LAE+29fJ8zXABMiBf+JtTAmhBBJDRFPzZo1sxPpDh06WIvnr776yloNc4LN6fXgwYNdz549re0wpzucko4ePdrVrl071pefLKCV9oABA9zOnTtDzy1YsMBNnz7d7dq1y57/8MMPbaw4NaXNc/gJK+jEOrJs3rzZvfDCC+7xxx+3x5xMc4p97bXXujlz5tj4MB4LFy60dvU5c+a0yBDa1zM+tOUW0UV2E3/IbuIf2U38cejQIYtWv+WWW2zP9vzzz7uxY8da5BNROIULF3bt27d3LVu2dDly5LDvn4h2ngcicdKnTx/rP+Oc5pdffnFVq1a16MEpU6aEnl+1apXr27ev++STT9w999zjRo4cGYqsYi/tCbcfkbz5vzOqEEIkMzJlyuQee+wxV6tWLZc5c2bXqFEjWxxJ4yOtb+3atSZQsXjWrVvXpUmTxo0fP95VqlQptIhqMY0ev//+u/vpp59cxowZLaWlQoUK9jyCIRvNzz//3NIn2eCAdxAgfEzkIESW/PnzhxxrnOyZM2e677//3l166aXmuBGi36RJE1e5cmW3evVqS1caOHCg/fTIbqKH7CY+kd3EN7Kb+ASBFkHK79nYp/k0ySxZsphIxR6O0gv33Xef2717tx0kXnnllbG+9GQDtsC89eabb7q5c+e6OnXq2PMFChSwtMrLLrvMhEEOfYsWLZpAkALNacKj2VMIkWyhZsRbb73lRowYYU4CtaLYAHGfHHhqSzz55JPmULD5QZAKX0S1mEYP6hAQwUZEQadOndxrr71mzz/11FN2Wk3tAmpNMD4IiCLpufvuu+2E+v777zfHGieBSA/AdjjN9o51eFC27CZ6yG7iH9lN/CG7OTvgALFevXoWOYg4hWhYvnx5E6QAkRdBytuNkoGiD4e4Dz30kI0LkVHUaMVWypUr57p162bRU+vWrbM6ekS3CfFXKH1PCJHsITyc0zaKlbIpffHFF21hpWBj3rx5XaFChdx7771nmx+dhCZ9+D7FTF955RUTBzltIzw/V65cJihSIDNt2rRu6NChOh1NQsLTVHAI9u/f76pXr25FmrNnz26FaEl/5QRbJD2ym/hEdhPfyG7iG5/69eWXX5qt5MmTx914442WwqdIwthy+PBhsx3EJ1L6EKQefvhhe23+/PmWGkvReQqgU5xeiMQofU8IkewpWbKkddBBmAI6t+A4sLgSirxlyxbb/IjYhO9zgn3kyBHrhESIOA4Czh3OHBuhrVu3ykFIYrAP72DjqLVr185sCEehbdu2btCgQeZYy1GIDbKb+ER2E9/IbuIbBCmi2B944AHXtWtXizJE7KA+KKmXIrYRU8xnpMJefvnlFg3qoeYUNkT6ngQp8VcoUkoIIcIipm6//XZrKUwqHzWk2PTwGFTENHbgDPiNj29BnNhpkyOX9ITbRMeOHd2sWbPsJJQUJI1H7JHdxCeym/hGdhOf8J2TWrlv3z4Tcim9QAF60ixFfHD8+HGXOnXq0DyHjchOxL9BkVJCCBEWMUWaHt1dSKUg9JgaBh4JUrED58BvShMXyvRo4xPbyA86U+Is/Pjjj/aaxiP2yG7iE9lNfCO7iU/4zqm15sdkyZIlLl++fLG+LBGGF6RAe2ZxOihSSgghEkGrbrrteXQiKsS/j/yQvQjx75DdCPHfIMWSOl8VK1a0iHYhxNmNRCkhhBBCnDFyqoU4fWQ3Qvw3VFJBiHMHiVJCCCGEEEIIIYQQIsmRvCyEEEIIIYQQQgghkhyJUkIIIYQQQgghhBAiyZEoJYQQQgghhBBCCCGSHIlSQgghhBBCCCGEECLJkSglhBBCCBEhDhw44LZt2xbryxBCCCGEOCtQ9z0hhBBCiAhx3XXXuTRp0rjly5f/q/d/9NFH7pprrnHp0qVL8Pxzzz3nSpUq5YoVK/aPn3Hw4EGXMWPG0ONx48a5cuXKuaJFi9rjZcuWuUKFCrls2bKd1FJ95cqVp/zMfPnyuUsvvTTBcwsXLnQzZ85006ZN+1d/mxBCCCHEP6FIKSGEEEKICNGsWTP3wQcfuA0bNvyr9zdq1Mh16tTppOeffPJJ9/HHH//j7+/du9flyZPHvfDCC6Hn+vfv7z777DO7/+OPP7oGDRqc8t8AxKtbb73V3X777aEbz82bN++k9+7Zs8ctWbLkX/1dQgghhBD/BolSQgghhBCnyYoVK1yKFClOunXu3Nlev/rqq0967fzzz0/wGQhX27dvd+3atTvp81OlSuVSp079j9eRPXt2N2DAANe8eXM3Y8YMey5lypT2u8eOHXN169Z1uXPndlOmTDnpd8877zy7LVq0yB09ejR0y5UrV+haJ06c6Fq2bBn6XN4vhBBCCBEpUkXsk4QQQgghkglp06YNpcadKsXujz/+MBHHM3z4cDdq1KgE75k0aZIrXLiwpdnt27cvgWhFdYXffvvN/fLLL6HHv//+u4lQgHi0detWd8EFF7iqVataxBXv/fbbb+3f/uGHH9ynn35qkVJTp061z+f3M2TI4HLkyBH6dxDLToUXn/icjRs3nuG3JYQQQghxaiRKCSGEEEKcJohCgMizf/9+9/LLL7uHHnrIIpwgf/78rkmTJq5fv372+JJLLnEXXnhh6PcRnKZPn+5q1Khh4tGNN9540r/Rpk0bu4Vz/Phx+zcQikqWLGkRUb4eFSl3Pk2vZ8+eoUirKlWqmKhF5NT999+fQByjrtQ333zjsmTJkuDf8CVHFR0lhBBCiGiiXYYQQgghxGly/fXXu59//tmipL7++ms3YsQIK0zu60AhWp04cSL0/q5du7rvv/8+9HjChAnup59+ChVHRzBCCPriiy9MCCKF7qqrrjIRiecRjxCyvOhFcXT+DaKfiJDi1qJFC0vVu/jiiy1dr0+fPq5OnToWUUVXQN4fLkh58Yn0PP4Of+M6uR4hhBBCiGgjUUoIIYQQ4jTxNZ+ox0T6HJFLFByvWbOmiUeIRVdcccUpfxcxatCgQaHXEaF86t5LL71kgtdFF11kUVREYCEQkWZHVz8P0Uuk7gGpefy7r776qps/f76lFiJiUbScgud03ps1a9ZJ18H1I0xxI5oKwco/btWqVZS+OSGEEEKI/49EKSGEEEKI/8Dbb7/tqlWr5u666y4TjebOnWud9xCFDh065K688spT/t6YMWNc3rx5Xf369RM8Txrg2LFjQ4IQ4hRC17Bhw075OZs2bXKPPPKIy5cvn/37q1evNgEKEYsb6X1EbhFtdffdd1vRc6K7Eotr3DZv3myRVP7xX9WaEkIIIYSIJBKlhBBCCCH+Aw0aNHCLFy82IeqGG26wAuOITURCES1FCt6p6NWrl0UuJa7VRE0qIqTuueee0HODBw+22yeffJLgvfxbpN0tWLDAPuv1118PFUGn85+veUU0FFFZCGYITeF1rSpVqmQ3oqR2797tpk2bZvf9bf369RH9voQQQgghEqNC50IIIYQQpwld7ogoonseEVNEJNGpDryYQ3rdnj177D7pdEQvZcyY0WXOnNnqRYWDcPT888+7JUuWhAqUA6IRxc75+cYbb7ibb77Znj948KAbOXKkFShH3OJ6PKTzcQ3hz1GDCnGL+lJEU0G5cuXsdxGrFi5caJFZBQoUsNeoR+WFLSGEEEKIaCFRSgghhBDiNEEc2rVr19++hw58iUHsGThwYOgxEVWk602dOtWKpRcvXtxqOnkQs3g/xcfLly9vHftIxaN+VPv27U3AoiZVeATV4cOHLR1vxowZoecpaE6dK7oA+ut+9NFH7eeWLVvsuujYhyjFe4nm4rMR0sKvRwghhBAikih9TwghhBDiNKHjHmINohEijr9t377dut8hMhHN5J/nvb/++mtICPIQpcRt0qRJLlu2bBZJRdFzuvA1a9bMBKfq1au72bNnW8e+xo0b2+8hZBHJRO0q332PG13+iMKirhW/45+n+x4C2KmENFL/iLgi9RB4H6RLl87uqxOfEEIIIaKFRCkhhBBCiNMkffr0JxUEJ4WPaCZupMLlzJnTtW3b1u3du9fei8jjO+Z56Kg3efJk17p1a1enTh23c+dOS78rUqSIGz9+vNWneuWVV+zf4T2J61CFQyfAIUOGWDQVn9moUSOLqPo7duzY4YYOHWoil4+48qIUwhaf9/nnn5/htyWEEEIIcWokSgkhhBBC/EeIgKKmFEXPKXZesWJFi1AqWLCgddJ75513rDtev379LFIqHKKsuIULXQhZWbNmtegqBCzS7ahB9XeQfte5c2dXu3ZtN3HiREstRJCaMmWKFU2vV6+eW7ly5Um/t2HDBnfbbbe5TJkyWboe/+aRI0fcsmXLTAQjaiscalSFpwoKIYQQQpwpEqWEEEIIIU6T5cuXu2rVqlnHO9LriGiiWDhpe4hJREY1bdrUhB9qNT3xxBNWr2nbtm2hzyDq6a9EHlLm/q7Q+Lp161yXLl1MCONzV69ebZ0AmzdvHnoPYtSaNWtMaCpbtqzVuPL1rNauXetKlixpQhTCGcIUhdrp/le3bl1Xq1atUKe+efPmWXofqYdFixaN4LcohBBCiOROioDdiBBCCCGEOC3atWtn9Zvq169/Uje9U9Wgeu2111yPHj0i8m8jglWtWtXdeOONrkmTJq5MmTJ/+34ipahb1bBhQ/s9GDNmjEVUITh5iPLKlSuXiV0+NZF0vt69e9tzNWrUsIguIYQQQohIIFFKCCGEEEIIIYQQQiQ5St8TQgghhBBCCCGEEEmORCkhhBBCCCGEEEIIkeRIlBJCCCGEEEIIIYQQSY5EKSGEEEIIIYQQQgiR5EiUEkIIIYQQQgghhBBJjkQpIYQQQgghhBBCCJHkSJQSQgghhBBCCCGEEEmORCkhhBBCCCGEEEIIkeRIlBJCCCGEEEIIIYQQLqn5P6CLIkJ90+4tAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 设置中文字体（解决中文显示问题）\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei', 'DejaVu Sans']  # 用来正常显示中文标签\n",
    "plt.rcParams['axes.unicode_minus'] = False  # 用来正常显示负号\n",
    "def plot_basic_bar(f1_scores):\n",
    "  \"\"\"绘制基础柱状图\"\"\"\n",
    "  categories = list(f1_scores.keys())\n",
    "  scores = list(f1_scores.values())\n",
    "  \n",
    "  plt.figure(figsize=(12, 8))\n",
    "  bars = plt.bar(categories, scores, color='skyblue', edgecolor='navy', alpha=0.7)\n",
    "  \n",
    "  # 添加数值标签\n",
    "  for bar, score in zip(bars, scores):\n",
    "      plt.text(bar.get_x() + bar.get_width()/2, bar.get_height() + 0.005, \n",
    "              f'{score:.3f}', ha='center', va='bottom', fontsize=10)\n",
    "  \n",
    "  plt.title('中医药命名实体识别 - 各实体类别F1分数', fontsize=16, fontweight='bold', pad=20)\n",
    "  plt.xlabel('实体类别', fontsize=12)\n",
    "  plt.ylabel('F1分数', fontsize=12)\n",
    "  plt.xticks(rotation=45, ha='right')\n",
    "  plt.ylim(0.5, 0.9)  # 设置y轴范围，突出差异\n",
    "  plt.grid(axis='y', alpha=0.3, linestyle='--')\n",
    "  plt.tight_layout()\n",
    "  plt.show()\n",
    "\n",
    "plot_basic_bar(f1_scores)"
   ]
  }
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